{"id":"https://openalex.org/W4415088402","doi":"https://doi.org/10.1109/icmla66185.2025.00222","title":"Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models","display_name":"Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4415088402","doi":"https://doi.org/10.1109/icmla66185.2025.00222"},"language":"en","primary_location":{"id":"doi:10.1109/icmla66185.2025.00222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.11449","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086623071","display_name":"Shriyank Somvanshi","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shriyank Somvanshi","raw_affiliation_strings":["Texas State University,Ingram School of Engineering,San Marcos,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,Ingram School of Engineering,San Marcos,Texas,USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119958874","display_name":"Pavan Hebli","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pavan Hebli","raw_affiliation_strings":["Texas State University,Ingram School of Engineering,San Marcos,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,Ingram School of Engineering,San Marcos,Texas,USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116405783","display_name":"Gaurab Chhetri","orcid":"https://orcid.org/0009-0000-0124-4814"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gaurab Chhetri","raw_affiliation_strings":["Texas State University,College Science and Engineering,San Marcos,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,College Science and Engineering,San Marcos,Texas,USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053621729","display_name":"Subasish Das","orcid":"https://orcid.org/0000-0002-1671-2753"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subasish Das","raw_affiliation_strings":["Texas State University,Ingram School of Engineering,San Marcos,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,Ingram School of Engineering,San Marcos,Texas,USA","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086623071"],"corresponding_institution_ids":["https://openalex.org/I13511017"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2656219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1460","last_page":"1465"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crash","display_name":"Crash","score":0.8816999793052673},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5008999705314636},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47940000891685486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4634999930858612},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.453900009393692},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42500001192092896},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.42250001430511475},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4138000011444092}],"concepts":[{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.8816999793052673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.546999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5218999981880188},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47530001401901245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4138000011444092},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32919999957084656},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32580000162124634},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C3017903533","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Motor vehicle crash","level":4,"score":0.2903999984264374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2986542766","wikidata":"https://www.wikidata.org/wiki/Q2090494","display_name":"Vehicle safety","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icmla66185.2025.00222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.11449","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.11449","pdf_url":"https://arxiv.org/pdf/2509.11449","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:arXiv.org:2509.11449","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2509.11449","pdf_url":"https://arxiv.org/pdf/2509.11449","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.11449","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.11449","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.11449","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.11449","pdf_url":"https://arxiv.org/pdf/2509.11449","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"presents":[2],"a":[3],"deep":[4,89,127],"tabular":[5,90,128],"learning":[6],"framework":[7],"for":[8,26,98,130],"predicting":[9],"crash":[10,19,31,51,132,142],"severity":[11,99,133],"in":[12,110,140],"electric":[13],"vehicle":[14],"(EV)":[15],"collisions":[16],"using":[17,38],"real-world":[18],"data":[20],"from":[21],"Texas":[22],"(2017-2023).":[23],"After":[24],"filtering":[25],"electric-only":[27],"vehicles,":[28],"23,301":[29],"EV-involved":[30],"records":[32],"were":[33,96],"analyzed.":[34],"Feature":[35],"importance":[36],"techniques":[37],"XGBoost":[39],"and":[40,54,79,94,135],"Random":[41],"Forest":[42],"identified":[43],"intersection":[44],"relation,":[45],"first":[46],"harmful":[47],"event,":[48],"person":[49],"age,":[50],"speed":[52],"limit,":[53],"day":[55],"of":[56,126],"week":[57],"as":[58],"the":[59,124],"top":[60],"predictors,":[61],"along":[62],"with":[63],"advanced":[64],"safety":[65,138],"features":[66],"like":[67],"automatic":[68],"emergency":[69],"braking.":[70],"To":[71],"address":[72],"class":[73],"imbalance,":[74],"Synthetic":[75],"Minority":[76],"Over-sampling":[77],"Technique":[78],"Edited":[80],"Nearest":[81],"Neighbors":[82],"(SMOTEENN)":[83],"resampling":[84],"was":[85],"applied.":[86],"Three":[87],"state-of-the-art":[88],"models,":[91],"TabPFN,":[92],"MambaNet,":[93],"MambaAttention,":[95],"benchmarked":[97],"prediction.":[100],"While":[101],"TabPFN":[102],"demonstrated":[103],"strong":[104],"generalization,":[105],"MambaAttention":[106],"achieved":[107],"superior":[108],"performance":[109],"classifying":[111],"severe":[112],"injury":[113],"cases":[114],"due":[115],"to":[116],"its":[117],"attention-based":[118],"feature":[119],"reweighting.":[120],"The":[121],"findings":[122],"highlight":[123],"potential":[125],"architectures":[129],"improving":[131],"prediction":[134],"enabling":[136],"data-driven":[137],"interventions":[139],"EV":[141],"contexts.":[143]},"counts_by_year":[],"updated_date":"2026-05-02T08:42:23.175194","created_date":"2025-10-12T00:00:00"}